Definitions
‘Stroke’ as used herein refers to a sequence of time-ordered two-dimensional data points forming a distinct part of a digital ink sequence.
‘Stroke sequence’ as used herein refers to a time-ordered sequence of strokes.
‘Substroke’ as used herein refers to segmented components of a stroke.
The increasing use of pen computing and the emergence of paper-based interfaces to networked computing resources (for example see: P. Lapstun, Netpage System Overview, Silverbrook Research Pty Ltd, 6 Jun., 2000; and, Anoto, “Anoto, Ericsson, and Time Manager Take Pen and Paper into the Digital Age with the Anoto Technology”, Press Release, 6 Apr., 2000), has highlighted the need for techniques which are able to store, index, and search (raw) digital ink. Pen-based computing allows users to store data in the form of notes and annotations, and subsequently search this data based on hand-drawn queries. However, searching handwritten text is more difficult than traditional text (e.g. ASCII text) searching due to inconsistencies in the production of handwriting and the stylistic variations between writers.
The traditional method of searching handwritten data in a digital ink database is to first convert the digital ink database and corresponding search query to standard text using pattern recognition techniques, and then to match the query text with the converted standard text in the database. Fuzzy text searching methods have been described, see P. Hall and G. Dowling, “Approximate String Matching”, Computing Surveys, 12(4), pp. 381-402, 1980, that perform text matching in the presence of character errors, similar to those produced by handwriting recognition systems.
However, handwriting recognition accuracy remains low, and the number of errors introduced by handwriting recognition (both for the database entries and for the handwritten query) means that this technique does not work well. The process of converting handwritten information into text results in the loss of a significant amount of information regarding the general shape and dynamic properties of the handwriting. For example, some letters (e.g. ‘u’ and ‘v’, ‘v’ and ‘r’, ‘f’ and ‘t’, etc.) are handwritten with a great deal of similarity in shape. Additionally, in many handwriting styles (particularly cursive writing), the identification of individual characters is highly ambiguous.
Pen-based computing systems provide a convenient and flexible means of human-computer interaction. Most people are very familiar with using pen and paper. This familiarity is exploited by known systems which use a pen-like device as a data entry and recording mechanism for text, drawings or calculations which are quite naturally supported by this medium. Additionally, written ink is a more expressive format than digital text, and ink-based systems can be language-independent. Moreover, the majority of published information is distributed in paper form, and most people prefer reading printed material to reading information on screen-based terminals. However, online applications and publishing systems have a number of advantages over pen and paper, such as the ability to provide information on demand, document navigation via hypertext, and the ability to search and personalize the information.
The Netpage system, see Silverbrook Research, Netpage System Design Description, 8 Sep. 2000, provides an interactive paper-based interface to online information by utilizing pages of invisibly coded paper and an optically imaging pen. Each page generated by the Netpage system is uniquely identified and stored on a network server, and all user interaction with the paper using the Netpage pen is captured, interpreted, and stored. Memjet digital printing technology, see Silverbrook Research, Memjet, 1999, facilitates the on-demand printing of Netpage documents, allowing interactive applications to be developed. The Netpage printer, pen, and network infrastructure provide a paper-based alternative to traditional screen-based applications and online publishing services, and supports user-interface functionality such as hypertext navigation and form input.
Netpage is a three-tiered system comprising a client layer, a service layer, and an application layer, as depicted in FIG. 21. The client layer contains the Netpage pen, Memjet printer, and a digital ink relay. Typically, the printer receives a document from a publisher or application provider via a broadband connection, which is printed with an invisible pattern of infrared tags that encodes each page with a unique identifier and the location of the tag on the page. As a user writes on the page, the imaging pen decodes these tags and converts the motion of the pen into digital ink, see Silverbrook Research, Netpage Pen Design Description, 27 Apr. 2000. The digital ink is transmitted over a wireless channel to a relay base station, and then sent to the service layer for processing and storage.
The service layer consists of a number of services that provide functionality for application development, with each service implemented as a set of network servers that provide a reliable and scaleable processing environment. The infrastructure provides persistent storage of all documents printed using the Netpage system, together with the capture and persistent storage of all digital ink written on an interactive page. When digital ink is submitted for processing, the system uses a stored description of the page to interpret the digital ink, and performs the requested actions by interacting with the applications that generated the document.
The application layer provides content to the user by publishing documents, and processes the digital ink interactions submitted by the user. Typically, an application generates one or more interactive pages in response to user input, which are transmitted to the service layer to be stored, rendered, and finally printed as output to the user. The Netpage system allows sophisticated applications to be developed by providing services for document publishing, rendering, and delivery, authenticated transactions and secure payments, handwriting recognition and digital ink searching, and user validation using biometric techniques such as signature verification.
As a result of the progress in pen-based interface research, handwritten digital ink documents, represented by time-ordered sequences of sampled pen strokes, are becoming increasingly popular [J. Subrahmonia and T. Zimmerman: Pen Computing: Challenges and Applications. Proceedings of the ICPR, 2000, pp. 2060-2066]. This representation of handwriting is called on-line as opposed to off-line where documents are represented by digital images. On-line handwriting typically involves writing in a mixture of writing styles (e.g. cursive, discrete, run-on etc.), a variety of fonts and scripts and different layouts (e.g. mixing drawings with text, various text line orientations etc.). Although it is possible to process (e.g. recognise) the handwritten data directly, i.e. as it is output from the device, the processing system would have to account for all the variability in the data—an admittedly difficult task. To reduce the variability in the data, a document preprocessing step is typically used prior to further operations on the data. One of the tasks of document preprocessing is to identify document (here handwritten page) parts which share some common attribute, e.g. they contain ink that belongs to a text line, or it has the same font size, or it is a drawing, etc. Another task is to remove some of the variability by normalising, e.g. for size, rotation or slant.
The present invention relates to text line extraction, that is segmenting out document parts that constitute lines of text. Given the text line segments, a skew may be estimated, i.e. the orientation of a geometric line parallel to the text line's accepted baseline with respect to the horizontal axis. Note that in unconstrained handwriting the baseline is not well defined and various approximations are typically accepted (e.g. the least squares approximation line fit through the local y-minima of strokes of horizontal lines or line parts). The normalisation (rotation) of digital ink to correct for the skew angle is called deskewing;
Most probably due to the difficulties of the handwritten character/word classification task itself and secondly due to the relatively slow (until recently) evolution of pen-based devices operating directly on digital ink, research in the past two decades has not given too much attention to line extraction and deskewing for unconstrained on-line handwriting (the reader should distinguish line extraction from line segmentation which is often used to denote segmenting a text line into words and/or characters). In fact, only a single approach exclusively dealing with this problem was encountered in the literature [E. Ratzlaff, “Inter-line distance estimation and text line extraction for unconstrained on-line handwriting”, Proceedings of the 7th International Workshop on Frontiers in Handwriting Recognition, Sep. 11-13 2000, Amsterdam, Nijmegen: International Unipen Foundation, ISBN 90-76942-01-3, pp. 33-42], making use of temporal stroke relationships for line extraction. The method has been patented [M. Perrone and E. Ratzlaff, “Spatial sorting and formatting for handwriting recognition”, U.S. Pat. No. 6,333,994, IBM Corporation, December 2001] and an application has been described [A. Jain, A. Namboodiri and J. Subrahmonia, “Structure in On-line Documents”, Proceedings of the 6th International Conference on Document Analysis and Recognition, pp. 844-848, Seattle Wash., September 2001].
Unfortunately, Ratzlaff's approach poses restricting requirements as to the accepted writing style, thus limiting the applicability of the method to pages written in a single font size, with a constant inter-line distance between not significantly overlapping lines. Furthermore, lines are assumed to be approximately horizontal as the method is very sensitive to even small skew angles (2 degrees and above). Finally, the proposed algorithm relies on a number of manually selected parameters although, as the author argues, these could be automatically selected as a result of a training process (assuming that training data is available).
Similar limitations are common to off-line handwriting methods which are briefly discussed for the sake of completeness. Off-line approaches to handwritten line extraction and deskewing have to account for, apart from the writer's style variability, the global geometric transformation (especially the global skew angle) introduced by the imaging process. Partially due to the difficulty in telling the imaging skew from intentional angled writing, almost all methods have assumed that the original writing was meant to be horizontal, therefore a document page like the one shown in FIG. 4 may be problematic to handle. In addition, a common requirement in off-line methods is that all imaged ink is subject to the same skew—an assumption often violated (think of the image of two adjacent book pages in 2-column format scanned together by pressing the spine of the book). Moreover, many methods restrict the acceptable line orientation to a specified limited range (e.g. 45 degrees). A further limitation of off-line methods, mainly due to the lack of temporal information, is that not only are they sensitive to the overlap between lines but also they often require a minimum inter-line distance, larger than the maximum inter-stroke distance. Very often, a significant amount of writing is required for accurate results to be obtained. Finally, off-line methods are much more computationally expensive than on-line ones.
Despite their limitations, off-line methods have exploited a number of algorithms. In particular, variations of the projection profile method have been very popular for removing the global page skew of non-overlapping horizontal lines [H. Baird, “The Skew Angle of Printed Documents”, Proceedings of Society of Photographic Scientific Engineering, 1987, Vol. 40, pp. 21-24], [F. Venturelli, Z. Kovacs-Vajna, “A Successful Technique for Unconstrained Hand-Written Line Segmentation”, Progress in Handwriting Recognition, Ed. A. C. Downtown and S. Impedovo, World Scientific, pp. 563-568, 1997], [T. Steinherz, N. Intrator and E. Rivlin, “Skew Detection via Principal Components Analysis”, Proceedings of the 5th International Conference on Document Analysis and Recognition, 1999, pp. 153-156]. Local application of the projection profile method is a more accurate approach, however it is computationally expensive if a significant number of projections at different angles have to be computed for every local ink segment. The Hough transform has been extensively used for line extraction from document images [S. Srihari and V. Govindaraju. “Analysis of textual images using the Hough transform”, Machine Vision and Applications, 2:141-153, 1989], [L. Likforman-Sulem, A. Hanimyan, C. Faure, “A Hough based algorithm for extracting text lines in handwritten documents”, Third International Conference on Document Analysis and Recognition (Volume 2), pp. 774-777, Aug. 14-15, 1995], [Y. Pu and Z. In, “A Natural Learning Algorithm based on Hough Transform for Text Lines Extraction in Handwritten Documents”, Eighth International Workshop on Frontiers in Handwriting Recognition, KAIST Campus, Taejon City, Korea, Aug. 12-14, 1998, pp. 637-646], [J. Liang, I. Phillips and R. Haralick, “A Statistically based, Highly Accurate Text-line Segmentation Method”, Proceedings of the 5th International Conference on Document Analysis and Recognition, 20-22 Sep., 1999, Bangalore, India, pp 551-554]. Local application of the Hough transform for skewed horizontal lines [Y. Pu and Z. Shi, “A Natural Learning Algorithm based on Hough Transform for Text Lines Extraction in Handwritten Documents”, Eighth International Workshop on Frontiers in Handwriting Recognition, KAIST Campus, Taejon City, Korea, Aug. 12-14, 1998, pp. 637-646] is interesting. Using the Hough transform, short lines may be difficult to extract due to the limited number of points and therefore limited orientation information. An optimal quantisation of the (ρ,θ) transformation space would improve both speed and accuracy in detecting lines of a known fixed font size, however estimating the font size from a handwritten page of text lines with arbitrary orientation before line extraction is a yet unsolved problem. Finally, no systematic experiments have been presented using the Hough transform for lines of different arbitrary orientation within the same page and its speed for line extraction has not been reported.
Clustering of ink into stroke groups corresponding to text lines has also been proposed, using a nearest-neighbour clustering algorithm or the shortest spanning tree of the graph of connected components [S. Abuhaiba, S. Datta, M. Holt, “Line extraction and stroke ordering of text pages”, Third International Conference on Document Analysis and Recognition, Volume 1, pp. 390-394, Aug. 14-15, 1995]. Methods based on connected component clustering assume that the inter-stroke distance is smaller than the inter-line distance, something that cannot be guaranteed for handwritten documents. Some methods require a training set to generate probabilistic models of text line geometry on a page [J. Liang, I. Phillips and R. Haralick, “A Statistically based, Highly Accurate Text-line Segmentation Method”, Proceedings of the 5th International Conference on Document Analysis and Recognition, 20-22 Sep., 1999, Bangalore, India, pp 551-554]. Such an approach is not generally applicable in the unconstrained on-line handwriting case due to both the difficulty in modelling user behaviour and the lack of a reliable ground-truthing protocol for such data. A thinning-based image processing method has been proposed [S. Tsuruoka, Y. Adachi and T. Yoshikawa, “The segmentation of a text line for a handwritten unconstrained document using thinning algorithm”, Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition, Sep. 11-13, 2000, Amsterdam, pp. 505-510].
A method to extract curved horizontal lines based on local baseline segment estimates has been described [M. Feldbach and K. Tonnies. “Line Detection and Segmentation in Historical Church Registers”, In Sixth International Conference on Document Analysis and Recognition, pages 743-747, Seattle, USA, September 2001] for a specific type of handwritten document. A vertical descend method [A Hennig, N Sherkat and R J Whitrow, “Zone Estimation for Multiple Lines of Handwriting Using Approximating Spline Functions”, Progress in Handwriting Recognition, ed. A. C. Downton, S. Impedovo, pp. 63-67, World Scientific, Singapore, June 1997, ISBN 981-02-3084-2] requires horizontal lines of approximately same length. Texture-based, inter-line cross-correlation, direct least squares and Fourier-based methods have also been studied for printed text document processing [O. Okun, M. Pietikäinen and J. Sauvola, “Robust Document Skew Detection Based on Line Extraction”, Proceedings of the 11th Scandinavian Conference on Image Analysis, 1999, Jun. 7-11, Kangerlussuaq, Greenland, pp. 457-464]. However, the applicability of such methods in handwriting applications is limited due to the non-uniformity of the data.
Local processing is of importance for on-line handwriting data since one can reasonably assume that attributes like the font and the line orientation will not change significantly within most local spatial and/or temporal windows. Another advantage of local processing is that when new ink is added to a page, re-computation of the existing ink is not required.
Methods that utilise contextual information such as type of script, writing order or application environment are not considered. Although effective use of such knowledge would be beneficial for a specialised system, it may not be available for general-purpose systems like those designed for digital notepads.
A new method or system is needed for on-line handwritten text line extraction allowing orientation estimation without the limitations of Ratzlaff's method [E. Ratzlaff, “Inter-line distance estimation and text line extraction for unconstrained on-line handwriting”, Proceedings of the 7th International Workshop on Frontiers in Handwriting Recognition, Sep. 11-13 2000, Amsterdam, Nijmegen: International Unipen Foundation, ISBN 90-76942-01-3, pp. 33-42], i.e. able to detect lines in any orientation and possibly changing font characteristics and writing style.
This identifies a need for a method or system for line extraction in a digital ink sequence which overcomes or at least ameliorates problems inherent in the prior art.
The reference to any prior art in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that such prior art forms part of the common general knowledge.